Senior Python Engineer - Mainframe (DB2/JCL) Atlanta GA Onsite
Senior Python Engineer - Mainframe (DB2/JCL) Design & build Python on z/OS (USS): Develop CLI tools services and batch utilities that run natively on z/OS Unix System Services (USS) or orchestrate mainframe jobs from distributed hosts. Batch orchestration: Create and maintain JCL (PROCs symbolics condition codes GDGs) and integrate with enterprise schedulers (e.g. Control-M CA-7). Implement robust restart/recovery and step-level error handling. DB2 for z/OS engineering: Write and tune SQL; implement stored procedures; design schemas and indexes; use EXPLAIN RUNSTATS and utilities (LOAD/UNLOAD/REORG) to hit SLAs. Python DB2 integration: Connect via ibmdb/CLI/ODBC and optimize connection pooling cursor usage and transaction boundaries for high-throughput workloads. Data processing pipelines (plus): Build high-volume ETL/ELT flows with Python (e.g. pandas PyArrow Dask) and efficient I/O (binary formats streaming chunking). Job submission & automation: Submit and monitor jobs via SDSF z/OSMF REST APIs or Zowe CLI; parse JES output; automate handoffs and notifications. Reliability & observability: Implement structured logging metrics and tracing; integrate with enterprise monitoring (e.g. Splunk ELK). Testing & CI/CD: Enforce unit/integration tests (pytest) code reviews linting/type hints (flake8/ruff/mypy) packaging and CI/CD (e.g. Jenkins GitLab). Security & compliance: Follow RACF/Top Secret/ACF2 controls least privilege and data governance for PII/PCI/SOX environments. Mentorship & documentation: Coach engineers on Python mainframe best practices; produce clear runbooks and architecture docs. Required qualifications 8 years professional Python (3.x) building reliable performant production systems (CLIs services or batch). Hands-on Python on the mainframe (must-have): Comfortable with z/OS USS TSO/ISPF SDSF OMVS dataset concepts (PS/PO GDGs) code pages/EBCDIC vs ASCII and file I/O nuances. Mainframe DB2 expertise: Strong DB2 for z/OS (SQL tuning indexing strategies access paths utilities locking & concurrency). Familiar with SPUFI DSNTEP2 IBM Data Studio (or equivalents). Deep JCL proficiency: PROCs symbolic parms condition codes dataset allocation DFSORT/ICETOOL IDCAMS and common utilities; experience wiring JCL into enterprise schedulers. Scripting & OS: Shell (sh/bash) on USS; comfort with dataset/file conversions large file throughput and job logs. SDLC discipline: Git trunk-based development code reviews tickets/change management (e.g. Jira/ServiceNow). Excellent communicator with the ability to translate between mainframe data and app teams. Nice-to-have (strong plus) Data-processing Python: pandas PyArrow Dask; memory/perf tuning for large datasets; binary formats (Parquet/Avro). Automation toolchain: Zowe CLI z/OSMF REST IBM Z Open Automation Utilities Ansible for z/OS. Integration: IBM MQ CICS Kafka/CDC (e.g. IBM Data Replication) REST/gRPC services bridging z/OS and distributed. Performance engineering: SMF insights buffer pool tuning (with DBAs) batch window optimization. Refactoring legacy: Translating COBOL/PL/I batch logic to Python where appropriate; creating safe migration paths. Distributed data engines: PySpark/Spark (for off-platform processing) Airflow. Observability & SRE: SLA/SLO design incident response for nightly/weekly batch cycles. Tools & environment (illustrative) Mainframe: IBM z/OS 2.x USS TSO/ISPF SDSF RACF/Top Secret/ACF2 Data: DB2 12/13 for z/OS; utilities (REORG/RUNSTATS/LOAD/UNLOAD) Python: 3.x venv/poetry ibmdb/ODBC requests pandas/PyArrow (as applicable) Automation: Zowe CLI z/OSMF APIs Control-M/CA-7 Jenkins/GitLab CI Monitoring: Splunk/ELK enterprise log aggregation Success metrics Batch SLAs consistently met; reduced average job elapsed time and rerun rates Measurable DB2 query/perf improvements (e.g. CPU getpages elapsed time) Increased automation coverage (job submission/monitoring recovery) High test coverage and low change-failure rate across releases Education & certification BS/MS in CS EE or equivalent experience Nice-to-have: IBM Certified Database Admin - DB2 for z/OS; IBM z/OS Associate/Professional; Control-M/CA-7 certifications
Senior Python Engineer - Mainframe (DB2/JCL) Atlanta GA Onsite Senior Python Engineer - Mainframe (DB2/JCL) Design & build Python on z/OS (USS): Develop CLI tools services and batch utilities that run natively on z/OS Unix System Services (USS) or orchestrate mainframe jobs from di...
Senior Python Engineer - Mainframe (DB2/JCL) Atlanta GA Onsite
Senior Python Engineer - Mainframe (DB2/JCL) Design & build Python on z/OS (USS): Develop CLI tools services and batch utilities that run natively on z/OS Unix System Services (USS) or orchestrate mainframe jobs from distributed hosts. Batch orchestration: Create and maintain JCL (PROCs symbolics condition codes GDGs) and integrate with enterprise schedulers (e.g. Control-M CA-7). Implement robust restart/recovery and step-level error handling. DB2 for z/OS engineering: Write and tune SQL; implement stored procedures; design schemas and indexes; use EXPLAIN RUNSTATS and utilities (LOAD/UNLOAD/REORG) to hit SLAs. Python DB2 integration: Connect via ibmdb/CLI/ODBC and optimize connection pooling cursor usage and transaction boundaries for high-throughput workloads. Data processing pipelines (plus): Build high-volume ETL/ELT flows with Python (e.g. pandas PyArrow Dask) and efficient I/O (binary formats streaming chunking). Job submission & automation: Submit and monitor jobs via SDSF z/OSMF REST APIs or Zowe CLI; parse JES output; automate handoffs and notifications. Reliability & observability: Implement structured logging metrics and tracing; integrate with enterprise monitoring (e.g. Splunk ELK). Testing & CI/CD: Enforce unit/integration tests (pytest) code reviews linting/type hints (flake8/ruff/mypy) packaging and CI/CD (e.g. Jenkins GitLab). Security & compliance: Follow RACF/Top Secret/ACF2 controls least privilege and data governance for PII/PCI/SOX environments. Mentorship & documentation: Coach engineers on Python mainframe best practices; produce clear runbooks and architecture docs. Required qualifications 8 years professional Python (3.x) building reliable performant production systems (CLIs services or batch). Hands-on Python on the mainframe (must-have): Comfortable with z/OS USS TSO/ISPF SDSF OMVS dataset concepts (PS/PO GDGs) code pages/EBCDIC vs ASCII and file I/O nuances. Mainframe DB2 expertise: Strong DB2 for z/OS (SQL tuning indexing strategies access paths utilities locking & concurrency). Familiar with SPUFI DSNTEP2 IBM Data Studio (or equivalents). Deep JCL proficiency: PROCs symbolic parms condition codes dataset allocation DFSORT/ICETOOL IDCAMS and common utilities; experience wiring JCL into enterprise schedulers. Scripting & OS: Shell (sh/bash) on USS; comfort with dataset/file conversions large file throughput and job logs. SDLC discipline: Git trunk-based development code reviews tickets/change management (e.g. Jira/ServiceNow). Excellent communicator with the ability to translate between mainframe data and app teams. Nice-to-have (strong plus) Data-processing Python: pandas PyArrow Dask; memory/perf tuning for large datasets; binary formats (Parquet/Avro). Automation toolchain: Zowe CLI z/OSMF REST IBM Z Open Automation Utilities Ansible for z/OS. Integration: IBM MQ CICS Kafka/CDC (e.g. IBM Data Replication) REST/gRPC services bridging z/OS and distributed. Performance engineering: SMF insights buffer pool tuning (with DBAs) batch window optimization. Refactoring legacy: Translating COBOL/PL/I batch logic to Python where appropriate; creating safe migration paths. Distributed data engines: PySpark/Spark (for off-platform processing) Airflow. Observability & SRE: SLA/SLO design incident response for nightly/weekly batch cycles. Tools & environment (illustrative) Mainframe: IBM z/OS 2.x USS TSO/ISPF SDSF RACF/Top Secret/ACF2 Data: DB2 12/13 for z/OS; utilities (REORG/RUNSTATS/LOAD/UNLOAD) Python: 3.x venv/poetry ibmdb/ODBC requests pandas/PyArrow (as applicable) Automation: Zowe CLI z/OSMF APIs Control-M/CA-7 Jenkins/GitLab CI Monitoring: Splunk/ELK enterprise log aggregation Success metrics Batch SLAs consistently met; reduced average job elapsed time and rerun rates Measurable DB2 query/perf improvements (e.g. CPU getpages elapsed time) Increased automation coverage (job submission/monitoring recovery) High test coverage and low change-failure rate across releases Education & certification BS/MS in CS EE or equivalent experience Nice-to-have: IBM Certified Database Admin - DB2 for z/OS; IBM z/OS Associate/Professional; Control-M/CA-7 certifications